Analysis of Risk and Returns Relationship in Australian Stock Market Exchange - Example

Analysis of Risk and returns Relationship in Australian stock Market Exchange Student’s Name: Institution’s Name: Lecturer’s Name: Course Code: Date of Submission: Contents Introduction 4 Overview of risk and return relationship 4 Literature Review 6 Results of the Analysis 11 It is appropriate to recognize that average stock risk is computed in the same manner as the weighted average of all the available beta of stock when 988 stocks are examined (Mossin, 1966). Because the mean beta is 0.98 which is less than 1, it is important to note that it can be described by the diversification theory which provides a provision that effective investment of securities in different investment of 50 securities is able to remove volatilities available in the investment returns but can only leave that which is influenced by the effect of systematic risk (Pettengill Et al, 1995). In relation to the result above, the mean beta of 0.98 show that when there is an increase or decrease in general market by 2% there would be an average variation of 0.98% diversification of 988 securities. This indicates that there is a lower systematic risk in the portfolio than that in the general market. The table above indicates that when there is higher beta risk there is high asset returns as provided by the analysis of CAMP but this situation does always not remain as provide since sometimes there is a reverse (Roenfeldt, 1978). There is also some evidence which show that there is possibility of higher beta able to produce low investment returns on security assets (Baesel & Jerome, 1974). In the a above results it is illustrated that 181stocks among 988 are of high beta as compared to market risk which was evaluated by systematic risk and able to produce more security asset returns that current market index (Sharpe, 1964). 17.2% which was found to be higher than 27.8% of securities which was determined in the analysis that produced high beta but generated low security assets returns. In this analysis there are 36.5% of the used securities that produce beta which is less than 1 but able to provide returns that is more than market index. The number of securities which was analyze composed of 27.8% and 36.5% totaling to 53.7% that produced reverse result to CAMP hypothesis that asset with high risk beta is able to produce more asset returns (Fletcher, 2000). 12 The result produced also show that only 18.3% and 17% of the evaluated securities show that high risky beta is able to produce high returns in relation to the requirement of CAMP. Others which covers 46.3% produced the reverse result as prescribed by the CAMP where assets with higher risk beat produces low returns and those of lower risk beta generates high returns as compared to the mean. The determined outcome reverses the expected results of CAMP because the correlation between risk and returns of investment portfolios with the same weights do not produce the required result. In the literature review there are some points which explain the contradiction which is produced in the above result. The main problem originates from the computation of expected rate of return. This produces the problem when evaluating the validity of CAMP (Strong, 1997). This model of CAMP demands that all the available input data in the analysis are historical and this causes biasness when CAMP is used. This is explained in the literature review and the result also produces the same finding (Grundy & Malkiel, 1996). There is also a problem in the determination of beta which lacks scientific application. In the above results the determined beta was produced from 144 Months starting from January 2001 to December 2013. The literature review provides that there is a variation of beta in relation to different periods and therefore the result would have been more accurate when it was computed annually since it is able to smooth out seasonal effects (Pettengill Et al, 1995). The final problem is found when CAMP was used to approximate betas based on Australian market stock exchange composite index (Chan & Lakonishok, 1993). It produce that security risk can be produced in one model of the sensitivity of the capital market. In a comprehensive stock market index, the relationship between risk and returns require a multi- dimensional effects produced from different factors (Tinic & West, 1984). These factors are not limited to the size and the application of various ratios and economic factors but contain others which have an effect on the security asset returns. This is supported by the literature review which suggests that the security returns is deeply related to the value of market capitalization. 13 Conclusion 14 The development of CAMP and beta came about because there was no appropriate tool to set affective price to security assets. There existence has cause a lot of dilemma to different investors and this led to continuous discussion whether beta can produce a good measure of security risk and tend to ask for the validity of CAMP. This research paper tries to examine the availability of linear relationship between risk and return when 988 securities were used from 2001 to 2013. The companies whose securities were used were listed in the stock exchange of Australia. CAMP model is able to produce the expected results of a positive trade off between market risk and returns. It results complied with the expectation of most researchers in the above literature review that there is a strong negative correlation between beta and returns within 144 months of the period provided. The securities which were analyzed on Australian market stock exchange produced unexpected results to CAMP common norm which suggest that high risk investment security assets produce more returns and security asset with low beta risk produce less returns. In the above result security assets with low risk were able to produce more returns and also some of the securities with higher risks were also able to produce fewer returns. There are several provisions in the literature review which support the reverse trend of CAMP. The most appropriate evidence is found in the oversimplification of CAMP which provides that the risk of securities in one model is very sensitive to the market when a more comprehensive market is being used. The final remark about CAMP is that it is not the best model that can be used to show linear relationship between risk and security asset returns. This is because there are several argument and discussion which are against it. The only advantage of this model is that it a directive in which the investors can select security asset which is risky and produce high returns. 14 Introduction Overview of risk and return relationship Rationalizing risk and return is a serious issue that affects most potential investors. But the investors who have adequate knowledge, analytical tool and data is able to maximize their investment returns at any level of risk which chosen (Tinic & West, 1984). It is appropriate for each investor to compute the investment risk which he wants to use in relation to risk tolerance, financial situation and life span of the investment as life condition is able to ascertain the financial needs of the business (Kothari & Sloan, 1995). It is useful when there is a balance between risk tolerance and other factors that affect the investment returns unless you are very rich. Therefore it is vital not to have only one investment portfolio but to invest in several investments so as to mitigate the level of both financial and business risk. There are only two types of investment namely specific and systematic where specific is diversifiable but systematic is non diversifiable (Strong, 1997). The reason of assembling investments is to reduce the level of specific risks but it depends on the asset which the investor has selected to use. It is therefore important to analyze the concept of risk- return relationship because it has left most of the investors and portfolio managers into a dilemma. The commonly use tool for risk examination is (CAMP) which was founded in the early 1960s (Bernstein & Fabozzi, 1998). It suggest that systematic risks which is also called beta is the most appropriate tool that can be used to measure the level of investment risk and lead to expected trade off between beta and required returns on investment. It is determined that there is a linear relationship between investment asset returns and beta as provided by the CAMP hypotheses. The measure of beta is in the ratio of covariance of a single investment asset with the market investment collection to the variance to the variance of its portfolio. The primary objective of this research is to ascertain the ability of CAMPS to support its hypotheses. In this case it is important to use beta to provide an explanation on the variation in quoted companies in Australian stock exchange returns on their investment. About Australian Market Stock Exchange ASX limited is a registered company and listed in the stock exchange therefore it is able to sell its shares through the Australian stock exchange. It was formed in 2006 at a time when ASE and Sydney Future Exchange was merged together to form one universal company (Bernstein & Fabozzi, 1998). Today the annual earnings of the company are expected to be higher than $4.685 billion when it has a higher market capitalization of $1.6 trillion. Its ability to gain higher investment returns it is rated second after New York stock exchange which has been in the top for more than 100 years. The stock exchange is a very good brokerage house which enables listed companies to sell its secondary shares to the public. It also provides rules and regulations in which companies use so that they can comply with the corporate standards of governance. The stock exchange is also providing financial advice to listed companies on how they can improve their capital base. The performance of Australian stock exchange is very good when it is rated with the 57 Global capital markets. It appears top ten both in the equity size and bond market (Kothari & Sloan, 1995). This stock exchange is able to control more than 200 listed companies in Australia and ensure that they adhere to the rules and regulations of governance. Literature Review Risk is defined as all those factors that cause fluctuation on the expected returns of the investor in relation to financial analysis. Financial risk is able to affect the stock prices and reduce the investor’s returns. The assessment of risk is done through the examination of the cash flow statement which composed of different investment assets and these risks compose of systematic and unsystematic risks (Reinganum, 1981). Non systematic risk can only be reduced by investing in different investment project while systematic risk cannot o be reduced through this means. CAMP is able to accounts for the relationship that exist between systematic risk and the cost of capital when beta is applied as the tool used to measure the manner in which the investment returns of different companies change relative to the change of the stock market returns (Lintner, 1965). It is appropriate to use CAMP because it is able to generate accurate measure of investment risk which can be used by potential investors to compute the rate of returns which cannot make them expose their money at a high risk. This tool provides that a project which is more risky is able to generate high investment returns and those which are less risky have low returns (Black & Fischer, 1972). This is explained very well by the use of security market line which demonstrates that projects which are risky have high investment returns. The cost of capital which is used in CAMP increase its accuracy but the only dilemma is in the stability of beta (Kothari & Sloan, 1995). This is because the estimation of beta depends on the methodology used since all the analyses use past returns to compute historical beta. The historical beta which has been approximated is used to predict the future beta which could be misleading (Blume &Marchall, 1971). It is therefore evident that beta for a single bond or stock cannot be relied on over time. Roenfeldt et al, determines that variation in the estimate of beta over time when different information is used is not stable over time and it is also affected by the number of times it is computed. This is because security stability increases with the increase with the duration of estimation. There is also another factor which affects the stability of beta and this is explained in the recent research which provides that the testing of beta is done by the use of synthetic CAMP but not natural CAMP because of the impossibility to know the actual market portfolio (Lintner, 1965). It is determined that there is low beta in Australian stock as compared to projected CAMP and improved beta in Australian stock performed poorly in 1934. Similar result was determined in the study done by Haugen in the risk -return analysis and the features of 1000 Australian stock had high capitalization more than any other stock exchange in Australia that took place in the year 1974 and 1987 (Bernstein & Fabozzi, 1998). These analysis determined that the efficiency of the market portfolio is poor due to stock which are less risky tend to produce high returns on investment hence produce the result that disagree with the relationship between beta and returns (Roenfeldt, 1978). The analysis of Fama and French determined beta and returns relationship which indicated that low risk result into low returns and high risk has high returns. This result was determined when 50 companies listed in the Australian stock exchange were examined in 19th century. The monthly analysis was also done by Lakonishock and Shapiro in New York and it was determined that the returns on stock is indirect relationship in symmetric risk but there is a positive correlation in the value of market capitalization (Reinganum, 1981). The result was supported by the study of Strong that produced that there was a negative correlation between beta and returns in UK equities in the early 1960. It is determined that the cause of low beta stock which has high returns as compared to CAMP results to borrowing restrictions which involve margin rules, tax regulation that controls reduction of interest expense and bankruptcy rules that prevent creditors from getting future borrower’s income (Black & Fischer, 1972). Absence of these restrictions still makes the investors not to borrow aggressively and this leads to the increase of leverage of the firm and this encourage indirect lending to investors who are not willing to borrow directly. All the investors who are willing to get more market risk should bid up high beta stocks since this is able to make low beta stock to be more marketable and liked by many investors than high beta stocks (Fletcher, 2000). There is a great impact of mismeasurement of the collection of investment of the market resulting from the consideration of only domestic stocks but ignoring foreign stock and bias in the choice of stock that tends to have low beta. This is able to make the price of low beta stock to have low price than high beta stocks in the market. There is also a claim that there is factual evidence against the Sharpe-Lintner-Black (SLB) model that considers the restriction of borrowing. It determines that there is a strong relationship between projected investment returns and computed beta. This suggestion was provided by Fama and French in the year 1992 and it was supported by the research of Bernstein and Fabozzi who said that their contribution resulted from data mining that was done on the basis of unpredicted priced factors and the use of beta factor which had not been explained properly. The explanation of negative correlation between beta and returns is provided by research study of Pettengill et al. For the result to be as expected, it must alter the research methodology when assessing this relationship since the actual result is used in the evaluation instead of expected one (Mossin, 1966). The result which was obtained indicate that when there is a positive excess market returns, it is possible to have a strong correlation between beta and returns and when there is a weak market returns the relationship becomes weaker (Pettengill Et al, 1995). The same result was reached by Fletcher in the year 2000 which was based on the monthly returns. His research was based on MSCI indices which were done in 18 countries and with the Global MSCI index. It was determined by the above analysis that stock with high beta are more price sensitive that stock with low beta to excess market returns (Fama & French, 1992).. It also provided that their returns tend to be lower than stocks that have low beta. It was provided by Pettengill et al that between 1936 to 1990 betas had a strong support in New York stock exchange returns in cases where the market is separated into either up or down markets periods. In addition to that Kothari, Shanken and Sloan found that the use of beta from the annual report instead on monthly report has a more genuine result because annual report beta is able to remove seasonal effects on the result (Baesel & Jerome, 1974). There was also another research which was done on the global stock returns in 1970 based on conditional correlation (Reinganum, 1981). It was carried out by Fletcher and he determined that there is a positive conditional correlation where nations with high beta were seen to have high investment returns than those with low beta in up market periods. He produced a strong emphasis on the importance of beta to foreign investors (Roenfeldt, 1978). The research of Fletcher also produced that the result of seasonal effects that affects conditional correlation of beta and returns. It indicates that there is a strong relationship in up periods and lack of relationship in down market periods (Fletcher, 2000). The above finding had been discovered by Tinic and West which illustrates that January is more risk premium as compared to the remaining months and a reasonable correlation only takes place in January between risk and required rate of returns (Pettengill Et al, 1995). In above explanation it was evident that risk premium vary insignificantly from zero when there was no January data in the analysis (Fama & French, 1992). This influenced the rejection of result of CAMP since it was determined to provide unrealistic result. It was further determined that the availability of only beta cannot provide accurate explanation of the correlation of Australian stock exchange returns ob stock when there is a turbulent market situation. This evidence was supported by Mile and Timmermann who conducted stock return in UK and because of the above results it is observed that asset pricing study tend to take a shift towards the use of a multifactor dimension where assets pricing is not subjected to only a single factor but it should include other functions of economic factors to produce a more comprehensive results (Sharpe, 1964). Currently there is only a single theory which explains the relationship between risk and returns when CAMP is used and it is called Arbitrage Pricing Theory (Lintner, 1965). This theory is able to include economic factors instead of only using systematic and unsystematic risk only Results of the Analysis Based on Mean Security Returns Mean 0.3941 High Low Total ≥0.3941 ≥0.3941 Based on Security Risk High ≥0.9837 181 297 478 48% Mean 0.98 low ≥0.9837 342 168 510 52% Total 523 465 988 100% 53% 47% 100% Based On market Index Returns Mean 0.4752 High Low Total ≥0.4752 ≥0.4752 Based on marker risk( beta) High ≥1 171 276 447 45% Beta 1.00 Low ≤ 1 363 184 547 55% Total   534 460 994 100% 54% 46% 100% Discussion It is appropriate to recognize that average stock risk is computed in the same manner as the weighted average of all the available beta of stock when 988 stocks are examined (Mossin, 1966). Because the mean beta is 0.98 which is less than 1, it is important to note that it can be described by the diversification theory which provides a provision that effective investment of securities in different investment of 50 securities is able to remove volatilities available in the investment returns but can only leave that which is influenced by the effect of systematic risk (Pettengill Et al, 1995). In relation to the result above, the mean beta of 0.98 show that when there is an increase or decrease in general market by 2% there would be an average variation of 0.98% diversification of 988 securities. This indicates that there is a lower systematic risk in the portfolio than that in the general market. The table above indicates that when there is higher beta risk there is high asset returns as provided by the analysis of CAMP but this situation does always not remain as provide since sometimes there is a reverse (Roenfeldt, 1978). There is also some evidence which show that there is possibility of higher beta able to produce low investment returns on security assets (Baesel & Jerome, 1974). In the a above results it is illustrated that 181stocks among 988 are of high beta as compared to market risk which was evaluated by systematic risk and able to produce more security asset returns that current market index (Sharpe, 1964). 17.2% which was found to be higher than 27.8% of securities which was determined in the analysis that produced high beta but generated low security assets returns. In this analysis there are 36.5% of the used securities that produce beta which is less than 1 but able to provide returns that is more than market index. The number of securities which was analyze composed of 27.8% and 36.5% totaling to 53.7% that produced reverse result to CAMP hypothesis that asset with high risk beta is able to produce more asset returns (Fletcher, 2000). The result produced also show that only 18.3% and 17% of the evaluated securities show that high risky beta is able to produce high returns in relation to the requirement of CAMP. Others which covers 46.3% produced the reverse result as prescribed by the CAMP where assets with higher risk beat produces low returns and those of lower risk beta generates high returns as compared to the mean. The determined outcome reverses the expected results of CAMP because the correlation between risk and returns of investment portfolios with the same weights do not produce the required result. In the literature review there are some points which explain the contradiction which is produced in the above result. The main problem originates from the computation of expected rate of return. This produces the problem when evaluating the validity of CAMP (Strong, 1997). This model of CAMP demands that all the available input data in the analysis are historical and this causes biasness when CAMP is used. This is explained in the literature review and the result also produces the same finding (Grundy & Malkiel, 1996). There is also a problem in the determination of beta which lacks scientific application. In the above results the determined beta was produced from 144 Months starting from January 2001 to December 2013. The literature review provides that there is a variation of beta in relation to different periods and therefore the result would have been more accurate when it was computed annually since it is able to smooth out seasonal effects (Pettengill Et al, 1995). The final problem is found when CAMP was used to approximate betas based on Australian market stock exchange composite index (Chan & Lakonishok, 1993). It produce that security risk can be produced in one model of the sensitivity of the capital market. In a comprehensive stock market index, the relationship between risk and returns require a multi- dimensional effects produced from different factors (Tinic & West, 1984). These factors are not limited to the size and the application of various ratios and economic factors but contain others which have an effect on the security asset returns. This is supported by the literature review which suggests that the security returns is deeply related to the value of market capitalization. Conclusion The development of CAMP and beta came about because there was no appropriate tool to set affective price to security assets. There existence has cause a lot of dilemma to different investors and this led to continuous discussion whether beta can produce a good measure of security risk and tend to ask for the validity of CAMP. This research paper tries to examine the availability of linear relationship between risk and return when 988 securities were used from 2001 to 2013. The companies whose securities were used were listed in the stock exchange of Australia. CAMP model is able to produce the expected results of a positive trade off between market risk and returns. It results complied with the expectation of most researchers in the above literature review that there is a strong negative correlation between beta and returns within 144 months of the period provided. The securities which were analyzed on Australian market stock exchange produced unexpected results to CAMP common norm which suggest that high risk investment security assets produce more returns and security asset with low beta risk produce less returns. In the above result security assets with low risk were able to produce more returns and also some of the securities with higher risks were also able to produce fewer returns. There are several provisions in the literature review which support the reverse trend of CAMP. The most appropriate evidence is found in the oversimplification of CAMP which provides that the risk of securities in one model is very sensitive to the market when a more comprehensive market is being used. The final remark about CAMP is that it is not the best model that can be used to show linear relationship between risk and security asset returns. This is because there are several argument and discussion which are against it. The only advantage of this model is that it a directive in which the investors can select security asset which is risky and produce high returns. Bibliography Baesel, R. & Jerome, B. 1974. On the Assessment of Risk: Some Further Consideration. Journal of Finance 29, No. 5, December, pp. 1491-1494. Bernstein, P. & Fabozzi, F. 1998. Beta and Return. The Best of the Journal of Portfolio Management, 74-77. Black, F & Fischer, H. 1972. Capital Market Equilibrium with Restricted Borrowing. Journal of Business. 45:3, pp. 444-454. Blume, E. Marchall, Y.1971. 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The conditional relation between beta and returns. Journal of Financial and Quantitative Analysis. 30(1), 101-116. Reinganum, M. 1981. A new empirical perspective on the CAPM. Journal of Financial and Quantitative Analysis. 16, 439–462. Roenfeldt, R. 1978. Further Evidence on the Stationarity of Beta Coefficients. Journal of Financial and Quantitative Analysis, March, pp 11 – 21. Sharpe, W. 1964. Capital asset price: a theory of market equilibrium under conditions of risk. Journal of Finance. 19, 425–442. Strong, N.1997. Explaining the cross-section of UK expected stock returns. British Accounting Review. 29, pp. 1–24. Tinic, S. & West, R. 1984. Risk and Return: January vs. the Rest of the Year. Journal of Financial Economics. (13), 561-674. Read More